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30 results for “variability detection”
Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars
<p>Supporting data for peer-reviewed publication entitled: 'Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars', published in A&A. For the purpose of open access, the authors have applied a CC BY licence to the author accepted manuscript version and made it publicly available: <a href="https://arxiv.org/abs/2410.12726">https://arxiv.org/abs/2410.12726</a></p> <p>Evolutionary models and stability window calculations courtesy of Jermyn et al. 2022 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac4e89">10.3847/1538-4357/ac4e89</a>) are publicly available via: <a href="https://github.com/adamjermyn/conv_trends">https://github.com/adamjermyn/conv_trends</a></p> <p>TESS full-frame image data are publicly available from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI): <a href="https://archive.stsci.edu/missions-and-data/tess">https://archive.stsci.edu/missions-and-data/tess</a></p> <p>TESS light curves (provided in this repository) were extracted using the publicly available tglc (Han & Brandt 2023; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/acaaa7">10.3847/1538-3881/acaaa7</a>) software package: <a href="https://github.com/TeHanHunter/TESS_Gaia_Light_Curve">https://github.com/TeHanHunter/TESS_Gaia_Light_Curve </a></p> <p>SLF variability parameters (provided in this repository; cf. Tables 1 and 2 of the paper) were obtained using GP regression with the publicly available celerite2 (Foreman-Mackey et al. 2017; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/aa9332">10.3847/1538-3881/aa9332</a>) software package: <a href="https://celerite2.readthedocs.io/en/latest/">https://celerite2.readthedocs.io/en/latest/</a> and confidence intervals were obtained using the publicly available pymc3 (Salvatier et al. 2016; <a href="https://doi.org/10.7717/peerj-cs.55">https://doi.org/10.7717/peerj-cs.55</a>) software package: <a href="https://github.com/pymc-devs/pymc">https://github.com/pymc-devs/pymc</a></p> <p>This research was supported in part by the National Science Foundation (NSF) under Grant Number NSF PHY-1748958; the Research Foundation Flanders (FWO) with grant agreement numbers 1286521N, 11F7120N, and V411621N; UK Research and Innovation (UKRI) in the form of a Frontier Research grant under the UK government's ERC Horizon Europe funding guarantee (SYMPHONY; grant number: EP/Y031059/1); a Royal Society University Research Fellowship (URF; grant number: URF\R1\231631); and the KU Leuven Research Council (grant number C16/18/005: PARADISE).</p>
Odontocete detections and corresponding values of environmental variables in the Hawaiian Archipelago
<p>This dataset contains detections of echolocation clicks at two sites in the Hawaiian Archipelago. These sites are Hawaii and Manawai (also known as Pearl and Hermes Reef). Echolocation clicks have been labeled using a neural network classifier that was trained and tested on data from the Hawaiian Islands and can successfully identify many species of regionally present odontocetes. During the labeling process, clicks were grouped into five-minute bins and each bin was given a class label. The data provided here is further binned at a daily level, where counts of a given class represent the number of five-minute bins within a given day that were labeled as that class. One file is provided per site, and files are in .csv format that can be read using any desired coding language. </p> <p>In addition to acoustic counts, values for environmental variables considered in the corresponding manuscript are provided in the CSV files. The final file in this dataset contains satellite-derived chlorophyll-a concentration values from NASA MODIS for the Hawaiian region (used to create Supplementary Fig. 1). Details on all variables and how they were accessed can be found in the manuscript and in the README file accompanying this dataset. </p>
Data and Code: Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway
<h3>Code</h3> <p><strong>MATLABWhaleDetectionCodeNWA.zip</strong></p> <p>The code used in the manuscript Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway<em> </em>(Goblot et al., in review)<em> </em>to detect whale calls in seismic waveforms was written by Alexandre Plourde and uploaded here with permission. This .zip file contains 3 folders, ‘Numerical Methods’, ‘Signal Processing’ and ‘Whale Subroutines’, as well as 3 main scripts (x2 for fin and blue whales). </p> <ul> <li>Pre-processing: <ul> <li>Convert .mseed to SAC files and rename with the format: YYYY.MM.DD.NETWORK.STATION..CHANNEL.SAC. Each SAC file must then be placed in a folder named with the format YYYYMMDD, referred to as events (evs) in the code. Each of these folders must then be placed into a single folder known as the events directory (drE) in the code.</li> </ul> </li> </ul> <ul> <li>Processing: Fin whales <ul> <li>Run ‘RecordWhaleNoiseLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘FinWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createFinWhaleDectectionList.m' to check which 120s time windows have W > threshold (3.0 in our case). These time windows are stored in the matrix FWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 120s time segments in the FWD matrix. The labelled call times are stored in the matrix FWC.</li> </ul> </li> </ul> <ul> <li>Processing: Blue whales <ul> <li>Run ‘RecordBlueWhalesLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘BlueWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createBlueWhaleDectectionList.m' to check which 720s time windows have W > threshold (1.5 in our case). These time windows are stored in the matrix BWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 720s time segments in the BWD matrix. The labelled call times are stored in the matrix BWC.</li> </ul> </li> </ul> <h3>Data</h3> <p>The whale detection code was applied to seismic waveform data downloaded through the EarthScope Consortium Web Services (<a href="https://service.iris.edu/" target="_blank" rel="noopener">https://service.iris.edu/</a>), including the following seismic network: CN (Natural Resources Canada, 1975). All whale calls were detected using the characteristic reccurence interval method (MATLABWhaleDetectionCodeNWA.zip).</p> <p><strong>Table S2</strong></p> <p><strong>WhaleDetectionsFeb2020Jan2022LSZ.mat</strong></p> <ul> <li>This dataset contains the center time of fin whale and blue whale detections and calls, from land 6 seismometers (CNQ, ICQ, SMQ, SNFQ, PMAQ, RISQ) in the Lower St-Lawrence Seaway between February 2020 and January 2022. <ul> <li>FWD_ <ul> <li>Column 1: list of fin whale detections (2 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWD_ <ul> <li>Column 1: list of blue whale detections (12 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>FWC_ <ul> <li>Column 1: list of individual ~1s fin whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWC_ <ul> <li>Column 1: list of individual ~8s blue whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>stadir: list of stations and labels (1-6)</li> </ul> </li> </ul> <p><strong>Table S3</strong></p> <p>a)<strong> MonthlyBlueWhaleDetectionsOct2015Jan2022.csv</strong> and b) <strong>MonthlyFinWhaleDetectionsOct2015Jan2022.csv</strong></p> <ul> <li> <div> <div> <div> <p>Number of monthly a) fin whale detections and b) blue whale detections from October 2015 to January 2022. Empty cells indicate periods when stations were not operating. Quiet day detections are included.</p> <p>Note the Oct 2015-Feb 2020 catalogue is from Plourde and Nedimović (2022) and includes up to 14 stations throughout this period from the following seismic networks: CN (Natural Resources Canada, 1975) and C8 (Natural Resources Canada, 2002). The Feb 2020-Jan 2022 catalogue is from Goblot et al. (in review) and includes the same stations from Table S1.</p> </div> </div> </div> </li> </ul> <h3>Additional</h3> <p><strong>Movie S1</strong></p> <p><strong>LSLSWhales.mp4</strong></p> <ul> <li>This movie contains an audiovisual representation of a series of fin whale calls and blue whale calls detected by 2 land seismometers in the Lower St-Lawrence Seaway. The fin whale detection was recorded by land seismometer CNQ (Côte-Nord) on Dec 12 2021 from 18:19:40 to 18:21:40. The blue whale detection was recorded by land seismometer SNFQ (Sainte-Félicité) on Aug 22 2021 from 05:36:00 to 05:48:00.</li> <li>The MATLAB soundsc(x) function was applied to seismic waveform data with fin whale and a blue whale calls. The signal with the fin whale detection is bandpassed from 18-21 Hz and the blue whale signal is bandpassed from 16-18 Hz. These signals were sped up 1000x in order to make them audible.</li> <li>The audio file was then uploaded to veed.io to produce a frequency response visualization of the whale calls.</li> <li>More audiovisuals can be viewed <a href="https://seismicsoundscapes.myportfolio.com/" target="_blank" rel="noopener">here</a>.</li> </ul>
Figs. 1–12 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 1–12. Diplomaragna terricolor (Attems, 1899). Variation in structure of the posterior angiocoxal process of posterior gonopod. 1 — from Anuchinsky District, Primorsky Krai; 2 — from Mt. Ko, Khabarovsky Krai; 3 — from Vladivostok, Okeanskaya Station, Primorsky Krai; (1st copy); 4 — from Vladivostok, Okeanskaya Station, Primorsky Krai; (2st copy); 5 — from Kedrovaya Pad Nature Reserve, Primorsky Krai; 6 — from Popova Island, Primorsky Krai; (1st copy); 7 — from Ussuriysky Nature Reserve, Primorsky Krai; 8 — from Lazovsky Nature Reserve, Primorsky Krai; 9 — from Sikhote-Alin Nature Biosphere Reserve (floodplain terrace, Yasnaya River), Primorsky Krai; 10 — from Chuguevsky District, Verkhneussuriysky Research Station, Primorsky Krai; 11 — from Popova Island, Primorsky Krai; (2st copy); 12 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scale in mm (0.5)
Figs. 13–16 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 13–16. Diplomaragna terricolor (Attems, 1899). Posterior angiocoxal processes of posterior gonopods. 13 — from Mt. Ko, Khabarovsky Krai; 14 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 15 — from Lazovsky Nature Reserve, Primorsky Krai; 16 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scales: 100 μm
Figs. 21–24 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 21–24. Diplomaragna terricolor (Attems, 1899). Coxae of male leg pair 11, front view. 21 — from Mt. Ko, Khabarovsky Krai; 22 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 23 — from Lazovsky Nature Reserve, Primorsky Krai; 24 — from SikhoteAlin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; p — coxal process. Scales: 100 μm Рис. 21–24. Diplomaragna terricolor (Attems, 1899). Коксы 11-й пары ног самца, виΑ спереΑи: 21 — с горы Ко в Хабаровском крае; 22 — из ВΛаΑивостока, станция Океанская; 23 — из Αазовского заповеΑника в Приморском крае; 24 — из СихотэАΛинского заповеΑника (урочище БΛагоΑатное, кΛюч Озёрный) в Приморском крае; р — коксаΛьный отросток. Масштабы: 100 μm
Figs. 17–20 in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Figs. 17–20. Diplomaragna terricolor (Attems, 1899). Gonopods, caudal view. 17 — from Mt. Ko, Khabarovsky Krai; 18 — from Vladivostok, Okeanskaya Station, Primorsky Krai; 19 — from Lazovsky Nature Reserve, Primorsky Krai; 20 — from Sikhote-Alin Nature Biosphere Reserve, Blagodatnoe, Ozerny Stream, Primorsky Krai; a — apical outgrowth; b — finger-shaped process; m — mesal protrusion. Scales: 100 μm
Map. Species diversity of Diplopoda within the Sikhote-Alin State Nature Biosphere Reserve. The numbers of the natural landmarks (1–18) are explained in the "Material and Methods section" Карта. ВиΑовое разнообразие Αвупарноногих многоножек в Сихотэ-АΛинском заповеΑнике. Номера урочищ (1–18) поясняются в разΑеΛе «МатериаΛ и метоΑы» in Review of the millipedes of the Sikhote-Alin State Nature Biosphere Reserve (Far East of Russia), with detection of the morphological variability of Diplomaragna terricolor (Attems, 1899) (Diplopoda)
Map. Species diversity of Diplopoda within the Sikhote-Alin State Nature Biosphere Reserve. The numbers of the natural landmarks (1–18) are explained in the "Material and Methods section" Карта. ВиΑовое разнообразие Αвупарноногих многоножек в Сихотэ-АΛинском заповеΑнике. Номера урочищ (1–18) поясняются в разΑеΛе «МатериаΛ и метоΑы»
Data for: Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers (Subterranean Biology, 2024)
<p>Original research article: Knüsel M., Alther R., Couton M. & Altermatt F. (2024) Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers. Subterranean Biology 49: 139-161. <a href="https://doi.org/10.3897/subtbiol.49.132515" target="_blank" rel="noopener">https://doi.org/10.3897/subtbiol.49.132515</a></p>
Key triggers of adaptive genetic variability of sessile oak [Q. petraea (Matt.) Liebl.] from the Balkan refugia: outlier detection and association of SNP loci from ddRAD-seq data
<p>Knowledge on the genetic composition of <em>Quercus petraea</em> in south-eastern Europe is limited despite the species' significant role in the re-colonisation of Europe during the Holocene, and the diverse climate and physical geography of the region. Therefore, it is imperative to conduct research on adaptation in sessile oak to better understand its ecological significance in the region. While large sets of SNPs have been developed for the species, there is a continued need for smaller sets of SNPs that are highly informative about the possible adaptation to this varied landscape. By using double digest restriction site associated DNA sequencing data from our previous study, we mapped RAD-tag sequences to the <em>Quercus robur</em> reference genome and identified a set of SNPs putatively related to drought stress-response. A total of 179 individuals from eighteen natural populations at sites covering heterogeneous climatic conditions in the southeastern natural distribution range of <em>Q. petraea</em> were genotyped. The detected highly polymorphic variant sites revealed three genetic clusters with a generally low level of genetic differentiation and balanced diversity among them but showed a north–southeast gradient. Selection tests showed nine outlier SNPs positioned in different functional regions. Genotype-environment association analysis of these markers yielded a total of 53 significant associations, explaining 2.4–16.6% of the total genetic variation. Our work exemplifies that adaptation to drought may be under natural selection in the examined <em>Q. petraea</em> populations.</p>
Wrist-worn sensor validation for heart rate variability and electrodermal activity detection in a stressful driving environment
<p>The current dataset contributes to assess the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Heart Rate Variability (HRV) and ElectroDermal Activity (EDA) signals were recorded over six experimental conditions (i.e., Baseline, Video Clip, Scream, No Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The raw quality of the physiological signals was enhanced by means of robust semi-automatic reconstruction algorithms. Heart Rate Variability time-domain parameters showed high accuracy in motion-free experimental conditions, while Heart Rate Variability frequency-domain parameters reported sufficient accuracy in almost every experimental condition.</p>
Odontocete detections and corresponding values of environmental variables in the Hawaiian Archipelago
Open the record for dataset details and reuse information.
Test and validation data for Robbie: A Batch Processing Work-flow for the Detection of Radio Transients and Variables
<p>Robbie: a general work-flow for the detection and characterization of radio variability and transient events in the image domain.<br> Robbie is designed to work in a batch processing paradigm with a modular design so that components can be swapped out or upgraded to adapt to different input data, whilst retaining a consistent and coherent methodological approach.<br> Robbie is based on commonly used and open software, and is encapsulated in a Makefile to aid portability and reproducibility.<br> In the description paper we describe the methodology behind Robbie, and demonstrate its use on real and simulated data.</p> <p>This repository contains the observed and simulated data that was used in the description paper.</p> <p> </p>
Supplementary Materials for "An Automated Detection of Confusing Variable Pairs with Highly Similar Compound Names in Java and Python Programs"
<p>This is a dataset contains the data collected through the empirical study in our paper "An Automated Detection of Confusing Variable Pairs with Highly Similar Compound Names in Java and Python Programs."</p>
Auxiliary supplementary material for the study on plant diversity relationships with satellite-detected phenological variability in USA wetlands at a national scale
Open the record for dataset details and reuse information.
Fermi/LAT detection of extraordinary variability in the gamma-ray emission of the blazar PKS 1510-089
Data set of the published paper. There are lightcurves in the gamma-ray energy band in QDP format (*.qdp; *.pco) and the corresponding PS figure. See the paper for details.
Fine-scale ecological and anthropogenic variables predict the habitat use and detectability of sloth bears in the Churia habitat of east Nepal
<p>Once widespread throughout the tropical forests of the Indian Subcontinent, the sloth bears have suffered a rapid range collapse and local extirpations in the recent decades. A significant portion of their current distribution range is situated outside of the protected areas (PAs). These unprotected sloth bear populations are under tremendous human pressures, but little is known about the patterns and determinants of their occurrence in most of these regions. The situation is more prevalent in Nepal where virtually no systematic information is available for sloth bears living outside of the PAs. We undertook a sign survey-based single-season occupancy study intending to overcome this information gap for the sloth bear populations residing in the Trijuga forest of southeast Nepal. Sloth bear sign detection histories and field-based covariates data were collected between 2nd October to 3rd December 2020 at the 74 randomly chosen 4-km^2 grid cells using a varying number of 400m long transects in each grid cell. From our results, the model-averaged estimate of site use probability (ψ ± SE) was estimated to be 0.432 ± 0.039, which is a 13% increase from the naïve estimate (0.297) not accounting for imperfect detections of sloth bear signs. The presence of termite mound and the distance to the nearest water source were the most important variables affecting the habitat use probability of sloth bears. The average site-level detectability (p ± SE) of sloth bear signs was estimated to be 0.195 ± 0.003 and was significantly determined by the index of human disturbances. We recommend considering the importance of fine-scale ecological and anthropogenic factors in predicting the sloth bear-habitat relationships across their range in the Churia habitat of Nepal, and more specifically in the unprotected areas.</p>
Variable Message Signal annotated images for object detection
<p><strong>If you use this dataset, please cite this paper: <em>Puertas, E.; De-Las-Heras, G.; Sánchez-Soriano, J.; Fernández-Andrés, J. Dataset: Variable Message Signal Annotated Images for Object Detection. Data 2022, 7, 41. https://doi.org/10.3390/data7040041</em></strong></p> <p>This dataset consists of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Also, a CSV file is attached with information regarding the geographic position, the folder where the image is located, and the text in Spanish. This can be used to train supervised learning computer vision algorithms, such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition, and labeling, and its specifications are detailed. The dataset is constituted of <strong>1216</strong> instances, <strong>888</strong> positives, and <strong>328</strong> negatives, in <strong>1152</strong> jpg images with a resolution of 1280x720 pixels. These are divided into <strong>576</strong> real images and <strong>576</strong> images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.</p> <p>The folder structure of the dataset is as follows:</p> <ul> <li>vms_dataset/ <ul> <li>data.csv</li> <li>real_images/ <ul> <li>imgs/</li> <li>annotations/</li> </ul> </li> <li>data-augmentation/ <ul> <li>imgs/</li> <li>annotations/</li> </ul> </li> </ul> </li> </ul> <p>In which:</p> <ul> <li><strong>data.csv:</strong> Each row contains the following information separated by commas (,): image_name, x_min, y_min, x_max, y_max, class_name, lat, long, folder, text.</li> <li><strong>real_images:</strong> Images extracted directly from the videos.</li> <li><strong>data-augmentation:</strong> Images created using data-augmentation</li> <li><strong>imgs:</strong> Image files in .jpg format.</li> <li><strong>annotations:</strong> Annotation files in .xml format.</li> </ul>
Retrospective Case-Control Study for Developing an Artificial Intelligence (AI) Tool for Lesion Detection Using Magnetic Resonance Imaging (MRI) and Clinical Variables for Early Diagnosis of Axial Spo
ClinicalTrials.gov study NCT06591481. IPD Sharing: NO. Countries: 4. Publications: 8.
Detection of Hypovolemia Using Pleth Variability Index (PVI)
ClinicalTrials.gov study NCT01456559. IPD Sharing: Not stated. Countries: 1. Publications: 6.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.